Nonlocal patch-based methods have been around for quite some time. In [1]}, the authors introduced a variant of nonlocal TV for denoising, inpainting, and compressive sensing. The optimization problem is solved using ADMM. Nonlocal TV is used for tomographic reconstruction in [2]}. In [3]}, a combination of nonlocal low-rank tensor decomposition and TV regularization is used for hyperspectral denoising; the model is solved using ADMM. Nonlocal low-rank regularization is used for compressive sensing in [4]}. The authors in [5]} used nonlocal intra- and inter-patch correlations for regularization purpose; the model is solved using IRLS algorithm. In [6]}, each patch is modeled as a sparse linear combination from an over-complete dictionary and this is used for the regularization of general image restoration tasks using ADMM. In [7]}, the problem of spectral unmixing is solved using nonlocal TV within the ADMM framework.
